My advice for getting into AI is to start with the basics. First off, you need a solid foundation in programming and computer science. Make sure you get comfortable with data structures, algorithms, and system design – they’re crucial no matter what you’re building.

Once you’ve got the basics down, dive into AI and machine learning through education or courses, and then find interesting projects for you. Try to work on small projects like image classification or sentiment analysis to get some hands-on experience.

Practical experience is key. Apply for internships in software engineering and AI to get real-world experience, and work on open-source projects or collaborate with experienced professionals. This kind of exposure is invaluable for learning best practices and getting insights from experts.

When it comes to full-stack AI projects, don’t have extensive experience in this specific area. Full-stack development with AI and LLMs is still in its early phases. In my view, AI tools are constantly evolving, and while it’s important to stay updated, it’s just as important to understand the capabilities, potential, limitations, and risks involved. Choosing the right tools and understanding their potential is key to building meaningful and successful projects.